{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import itertools\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score, roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary\n",
    "\n",
    "We will apply ensemble learning for face recognition models supported in [deepface for python](https://github.com/serengil/deepface).\n",
    "\n",
    "Human beings have 97.53% score in face recognition task based on the [Facebook study](https://research.fb.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification/). \n",
    "\n",
    "So, can ensemble of the most popular face recognition models have a higher score than human beings?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ref: https://github.com/serengil/deepface/tree/master/tests/dataset\n",
    "idendities = {\n",
    "    \"Angelina\": [\"img1.jpg\", \"img2.jpg\", \"img4.jpg\", \"img5.jpg\", \"img6.jpg\", \"img7.jpg\", \"img10.jpg\", \"img11.jpg\"],\n",
    "    \"Scarlett\": [\"img8.jpg\", \"img9.jpg\", \"img47.jpg\", \"img48.jpg\", \"img49.jpg\", \"img50.jpg\", \"img51.jpg\"],\n",
    "    \"Jennifer\": [\"img3.jpg\", \"img12.jpg\", \"img53.jpg\", \"img54.jpg\", \"img55.jpg\", \"img56.jpg\"],\n",
    "    \"Mark\": [\"img13.jpg\", \"img14.jpg\", \"img15.jpg\", \"img57.jpg\", \"img58.jpg\"],\n",
    "    \"Jack\": [\"img16.jpg\", \"img17.jpg\", \"img59.jpg\", \"img61.jpg\", \"img62.jpg\"],\n",
    "    \"Elon\": [\"img18.jpg\", \"img19.jpg\", \"img67.jpg\"],\n",
    "    \"Jeff\": [\"img20.jpg\", \"img21.jpg\"],\n",
    "    \"Marissa\": [\"img22.jpg\", \"img23.jpg\"],\n",
    "    \"Sundar\": [\"img24.jpg\", \"img25.jpg\"],\n",
    "    \"Katy\": [\"img26.jpg\", \"img27.jpg\", \"img28.jpg\", \"img42.jpg\", \"img43.jpg\", \"img44.jpg\", \"img45.jpg\", \"img46.jpg\"],\n",
    "    \"Matt\": [\"img29.jpg\", \"img30.jpg\", \"img31.jpg\", \"img32.jpg\", \"img33.jpg\"],\n",
    "    \"Leonardo\": [\"img34.jpg\", \"img35.jpg\", \"img36.jpg\", \"img37.jpg\"],\n",
    "    \"George\": [\"img38.jpg\", \"img39.jpg\", \"img40.jpg\", \"img41.jpg\"]\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Positive samples\n",
    "Find different photos of same people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "positives = []\n",
    "\n",
    "for key, values in idendities.items():\n",
    "    \n",
    "    #print(key)\n",
    "    for i in range(0, len(values)-1):\n",
    "        for j in range(i+1, len(values)):\n",
    "            #print(values[i], \" and \", values[j])\n",
    "            positive = []\n",
    "            positive.append(values[i])\n",
    "            positive.append(values[j])\n",
    "            positives.append(positive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "positives = pd.DataFrame(positives, columns = [\"file_x\", \"file_y\"])\n",
    "positives[\"decision\"] = \"Yes\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(140, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positives.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Negative samples\n",
    "Compare photos of different people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples_list = list(idendities.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "negatives = []\n",
    "\n",
    "for i in range(0, len(idendities) - 1):\n",
    "    for j in range(i+1, len(idendities)):\n",
    "        #print(samples_list[i], \" vs \",samples_list[j]) \n",
    "        cross_product = itertools.product(samples_list[i], samples_list[j])\n",
    "        cross_product = list(cross_product)\n",
    "        #print(cross_product)\n",
    "        \n",
    "        for cross_sample in cross_product:\n",
    "            #print(cross_sample[0], \" vs \", cross_sample[1])\n",
    "            negative = []\n",
    "            negative.append(cross_sample[0])\n",
    "            negative.append(cross_sample[1])\n",
    "            negatives.append(negative)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "negatives = pd.DataFrame(negatives, columns = [\"file_x\", \"file_y\"])\n",
    "negatives[\"decision\"] = \"No\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "negatives = negatives.sample(positives.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(140, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negatives.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge Positives and Negative Samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([positives, negatives]).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(280, 3)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     140\n",
       "Yes    140\n",
       "Name: decision, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.decision.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.file_x = \"deepface/tests/dataset/\"+df.file_x\n",
    "df.file_y = \"deepface/tests/dataset/\"+df.file_y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DeepFace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#!pip install deepface\n",
    "from deepface import DeepFace\n",
    "from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG-Face loaded\n",
      "Facenet loaded\n",
      "OpenFace loaded\n",
      "FbDeepFace loaded\n"
     ]
    }
   ],
   "source": [
    "pretrained_models = {}\n",
    "\n",
    "pretrained_models[\"VGG-Face\"] = VGGFace.loadModel()\n",
    "print(\"VGG-Face loaded\")\n",
    "\n",
    "pretrained_models[\"Facenet\"] = Facenet.loadModel()\n",
    "print(\"Facenet loaded\")\n",
    "\n",
    "pretrained_models[\"OpenFace\"] = OpenFace.loadModel() \n",
    "print(\"OpenFace loaded\")\n",
    "\n",
    "pretrained_models[\"DeepFace\"] = FbDeepFace.loadModel()\n",
    "print(\"FbDeepFace loaded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "instances = df[[\"file_x\", \"file_y\"]].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace']\n",
    "metrics = ['cosine', 'euclidean', 'euclidean_l2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing  VGG-Face   cosine\n",
      "Already built model is passed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Verification: 100%|██████████| 280/280 [11:31<00:00,  2.47s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing  VGG-Face   euclidean\n",
      "Already built model is passed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Verification: 100%|██████████| 280/280 [11:47<00:00,  2.53s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing  VGG-Face   euclidean_l2\n",
      "Already built model is passed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Verification: 100%|██████████| 280/280 [11:31<00:00,  2.47s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing  Facenet   cosine\n",
      "Already built model is passed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Verification: 100%|██████████| 280/280 [07:51<00:00,  1.69s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing  Facenet   euclidean\n",
      "Already built model is passed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Verification: 100%|██████████| 280/280 [07:46<00:00,  1.67s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing  Facenet   euclidean_l2\n",
      "Already built model is passed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Verification: 100%|██████████| 280/280 [07:51<00:00,  1.68s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing  OpenFace   cosine\n",
      "Already built model is passed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Verification: 100%|██████████| 280/280 [05:51<00:00,  1.25s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing  OpenFace   euclidean\n",
      "Processing  OpenFace   euclidean_l2\n",
      "Already built model is passed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Verification: 100%|██████████| 280/280 [05:58<00:00,  1.28s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing  DeepFace   cosine\n",
      "Already built model is passed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Verification: 100%|██████████| 280/280 [06:12<00:00,  1.33s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing  DeepFace   euclidean\n",
      "Already built model is passed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Verification: 100%|██████████| 280/280 [05:55<00:00,  1.27s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing  DeepFace   euclidean_l2\n",
      "Already built model is passed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Verification: 100%|██████████| 280/280 [05:56<00:00,  1.27s/it]\n"
     ]
    }
   ],
   "source": [
    "if True:\n",
    "    for model in models:\n",
    "        for metric in metrics:\n",
    "            \n",
    "            print(\"Processing \",model,\" \",metric)\n",
    "            \n",
    "            if model == 'OpenFace' and metric == 'euclidean': #this returns same with openface euclidean l2\n",
    "                continue\n",
    "            else:\n",
    "                resp_obj = DeepFace.verify(instances\n",
    "                                           , model_name = model\n",
    "                                           , model = pretrained_models[model]\n",
    "                                           , distance_metric = metric)\n",
    "\n",
    "                distances = []\n",
    "\n",
    "                for i in range(0, len(instances)):\n",
    "                    distance = round(resp_obj[\"pair_%s\" % (i+1)][\"distance\"], 4)\n",
    "                    distances.append(distance)\n",
    "\n",
    "                df['%s_%s' % (model, metric)] = distances\n",
    "    \n",
    "    df.to_csv(\"face-recognition-pivot.csv\", index = False)\n",
    "else:\n",
    "    #Ref: https://github.com/serengil/deepface/blob/master/tests/dataset/face-recognition-pivot.csv\n",
    "    df = pd.read_csv(\"face-recognition-pivot.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file_x</th>\n",
       "      <th>file_y</th>\n",
       "      <th>decision</th>\n",
       "      <th>VGG-Face_cosine</th>\n",
       "      <th>VGG-Face_euclidean</th>\n",
       "      <th>VGG-Face_euclidean_l2</th>\n",
       "      <th>Facenet_cosine</th>\n",
       "      <th>Facenet_euclidean</th>\n",
       "      <th>Facenet_euclidean_l2</th>\n",
       "      <th>OpenFace_cosine</th>\n",
       "      <th>OpenFace_euclidean_l2</th>\n",
       "      <th>DeepFace_cosine</th>\n",
       "      <th>DeepFace_euclidean</th>\n",
       "      <th>DeepFace_euclidean_l2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>deepface/tests/dataset/img38.jpg</td>\n",
       "      <td>deepface/tests/dataset/img39.jpg</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.2057</td>\n",
       "      <td>0.3890</td>\n",
       "      <td>0.6414</td>\n",
       "      <td>0.1601</td>\n",
       "      <td>6.8679</td>\n",
       "      <td>0.5658</td>\n",
       "      <td>0.5925</td>\n",
       "      <td>1.0886</td>\n",
       "      <td>0.2554</td>\n",
       "      <td>61.3336</td>\n",
       "      <td>0.7147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>deepface/tests/dataset/img38.jpg</td>\n",
       "      <td>deepface/tests/dataset/img40.jpg</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.2117</td>\n",
       "      <td>0.3179</td>\n",
       "      <td>0.6508</td>\n",
       "      <td>0.2739</td>\n",
       "      <td>8.9049</td>\n",
       "      <td>0.7402</td>\n",
       "      <td>0.3960</td>\n",
       "      <td>0.8899</td>\n",
       "      <td>0.2685</td>\n",
       "      <td>63.3747</td>\n",
       "      <td>0.7328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>deepface/tests/dataset/img38.jpg</td>\n",
       "      <td>deepface/tests/dataset/img41.jpg</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.1073</td>\n",
       "      <td>0.2482</td>\n",
       "      <td>0.4632</td>\n",
       "      <td>0.1257</td>\n",
       "      <td>6.1593</td>\n",
       "      <td>0.5014</td>\n",
       "      <td>0.7157</td>\n",
       "      <td>1.1964</td>\n",
       "      <td>0.2452</td>\n",
       "      <td>60.3454</td>\n",
       "      <td>0.7002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>deepface/tests/dataset/img39.jpg</td>\n",
       "      <td>deepface/tests/dataset/img40.jpg</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.2991</td>\n",
       "      <td>0.4567</td>\n",
       "      <td>0.7734</td>\n",
       "      <td>0.3134</td>\n",
       "      <td>9.3798</td>\n",
       "      <td>0.7917</td>\n",
       "      <td>0.4941</td>\n",
       "      <td>0.9941</td>\n",
       "      <td>0.1703</td>\n",
       "      <td>45.1688</td>\n",
       "      <td>0.5836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>deepface/tests/dataset/img39.jpg</td>\n",
       "      <td>deepface/tests/dataset/img41.jpg</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.1666</td>\n",
       "      <td>0.3542</td>\n",
       "      <td>0.5772</td>\n",
       "      <td>0.1502</td>\n",
       "      <td>6.6491</td>\n",
       "      <td>0.5481</td>\n",
       "      <td>0.2381</td>\n",
       "      <td>0.6901</td>\n",
       "      <td>0.2194</td>\n",
       "      <td>50.4356</td>\n",
       "      <td>0.6624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             file_x                            file_y  \\\n",
       "0  deepface/tests/dataset/img38.jpg  deepface/tests/dataset/img39.jpg   \n",
       "1  deepface/tests/dataset/img38.jpg  deepface/tests/dataset/img40.jpg   \n",
       "2  deepface/tests/dataset/img38.jpg  deepface/tests/dataset/img41.jpg   \n",
       "3  deepface/tests/dataset/img39.jpg  deepface/tests/dataset/img40.jpg   \n",
       "4  deepface/tests/dataset/img39.jpg  deepface/tests/dataset/img41.jpg   \n",
       "\n",
       "  decision  VGG-Face_cosine  VGG-Face_euclidean  VGG-Face_euclidean_l2  \\\n",
       "0      Yes           0.2057              0.3890                 0.6414   \n",
       "1      Yes           0.2117              0.3179                 0.6508   \n",
       "2      Yes           0.1073              0.2482                 0.4632   \n",
       "3      Yes           0.2991              0.4567                 0.7734   \n",
       "4      Yes           0.1666              0.3542                 0.5772   \n",
       "\n",
       "   Facenet_cosine  Facenet_euclidean  Facenet_euclidean_l2  OpenFace_cosine  \\\n",
       "0          0.1601             6.8679                0.5658           0.5925   \n",
       "1          0.2739             8.9049                0.7402           0.3960   \n",
       "2          0.1257             6.1593                0.5014           0.7157   \n",
       "3          0.3134             9.3798                0.7917           0.4941   \n",
       "4          0.1502             6.6491                0.5481           0.2381   \n",
       "\n",
       "   OpenFace_euclidean_l2  DeepFace_cosine  DeepFace_euclidean  \\\n",
       "0                 1.0886           0.2554             61.3336   \n",
       "1                 0.8899           0.2685             63.3747   \n",
       "2                 1.1964           0.2452             60.3454   \n",
       "3                 0.9941           0.1703             45.1688   \n",
       "4                 0.6901           0.2194             50.4356   \n",
       "\n",
       "   DeepFace_euclidean_l2  \n",
       "0                 0.7147  \n",
       "1                 0.7328  \n",
       "2                 0.7002  \n",
       "3                 0.5836  \n",
       "4                 0.6624  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 15))\n",
    "\n",
    "figure_idx = 1\n",
    "for model in models:\n",
    "    for metric in metrics:\n",
    "        \n",
    "        if model == 'OpenFace' and metric == 'euclidean': #this returns same with openface euclidean l2\n",
    "            metric = 'euclidean_l2'\n",
    "        \n",
    "        feature = '%s_%s' % (model, metric)\n",
    "\n",
    "        ax1 = fig.add_subplot(4, 3, figure_idx)\n",
    "        \n",
    "        df[df.decision == \"Yes\"][feature].plot(kind='kde', title = feature, label = 'Yes', legend = True)\n",
    "        df[df.decision == \"No\"][feature].plot(kind='kde', title = feature, label = 'No', legend = True)\n",
    "        \n",
    "        figure_idx = figure_idx + 1\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-processing for model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = []\n",
    "for model in models:\n",
    "    for metric in metrics:\n",
    "        if model == 'OpenFace' and metric == 'euclidean':\n",
    "            continue\n",
    "        else:\n",
    "            feature = '%s_%s' % (model, metric)\n",
    "            columns.append(feature)\n",
    "\n",
    "columns.append(\"decision\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df[df.decision == 'Yes'].index, 'decision'] = 1\n",
    "df.loc[df[df.decision == 'No'].index, 'decision'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VGG-Face_cosine</th>\n",
       "      <th>VGG-Face_euclidean</th>\n",
       "      <th>VGG-Face_euclidean_l2</th>\n",
       "      <th>Facenet_cosine</th>\n",
       "      <th>Facenet_euclidean</th>\n",
       "      <th>Facenet_euclidean_l2</th>\n",
       "      <th>OpenFace_cosine</th>\n",
       "      <th>OpenFace_euclidean_l2</th>\n",
       "      <th>DeepFace_cosine</th>\n",
       "      <th>DeepFace_euclidean</th>\n",
       "      <th>DeepFace_euclidean_l2</th>\n",
       "      <th>decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.2057</td>\n",
       "      <td>0.3890</td>\n",
       "      <td>0.6414</td>\n",
       "      <td>0.1601</td>\n",
       "      <td>6.8679</td>\n",
       "      <td>0.5658</td>\n",
       "      <td>0.5925</td>\n",
       "      <td>1.0886</td>\n",
       "      <td>0.2554</td>\n",
       "      <td>61.3336</td>\n",
       "      <td>0.7147</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2117</td>\n",
       "      <td>0.3179</td>\n",
       "      <td>0.6508</td>\n",
       "      <td>0.2739</td>\n",
       "      <td>8.9049</td>\n",
       "      <td>0.7402</td>\n",
       "      <td>0.3960</td>\n",
       "      <td>0.8899</td>\n",
       "      <td>0.2685</td>\n",
       "      <td>63.3747</td>\n",
       "      <td>0.7328</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.1073</td>\n",
       "      <td>0.2482</td>\n",
       "      <td>0.4632</td>\n",
       "      <td>0.1257</td>\n",
       "      <td>6.1593</td>\n",
       "      <td>0.5014</td>\n",
       "      <td>0.7157</td>\n",
       "      <td>1.1964</td>\n",
       "      <td>0.2452</td>\n",
       "      <td>60.3454</td>\n",
       "      <td>0.7002</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2991</td>\n",
       "      <td>0.4567</td>\n",
       "      <td>0.7734</td>\n",
       "      <td>0.3134</td>\n",
       "      <td>9.3798</td>\n",
       "      <td>0.7917</td>\n",
       "      <td>0.4941</td>\n",
       "      <td>0.9941</td>\n",
       "      <td>0.1703</td>\n",
       "      <td>45.1688</td>\n",
       "      <td>0.5836</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.1666</td>\n",
       "      <td>0.3542</td>\n",
       "      <td>0.5772</td>\n",
       "      <td>0.1502</td>\n",
       "      <td>6.6491</td>\n",
       "      <td>0.5481</td>\n",
       "      <td>0.2381</td>\n",
       "      <td>0.6901</td>\n",
       "      <td>0.2194</td>\n",
       "      <td>50.4356</td>\n",
       "      <td>0.6624</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VGG-Face_cosine  VGG-Face_euclidean  VGG-Face_euclidean_l2  Facenet_cosine  \\\n",
       "0           0.2057              0.3890                 0.6414          0.1601   \n",
       "1           0.2117              0.3179                 0.6508          0.2739   \n",
       "2           0.1073              0.2482                 0.4632          0.1257   \n",
       "3           0.2991              0.4567                 0.7734          0.3134   \n",
       "4           0.1666              0.3542                 0.5772          0.1502   \n",
       "\n",
       "   Facenet_euclidean  Facenet_euclidean_l2  OpenFace_cosine  \\\n",
       "0             6.8679                0.5658           0.5925   \n",
       "1             8.9049                0.7402           0.3960   \n",
       "2             6.1593                0.5014           0.7157   \n",
       "3             9.3798                0.7917           0.4941   \n",
       "4             6.6491                0.5481           0.2381   \n",
       "\n",
       "   OpenFace_euclidean_l2  DeepFace_cosine  DeepFace_euclidean  \\\n",
       "0                 1.0886           0.2554             61.3336   \n",
       "1                 0.8899           0.2685             63.3747   \n",
       "2                 1.1964           0.2452             60.3454   \n",
       "3                 0.9941           0.1703             45.1688   \n",
       "4                 0.6901           0.2194             50.4356   \n",
       "\n",
       "   DeepFace_euclidean_l2  decision  \n",
       "0                 0.7147         1  \n",
       "1                 0.7328         1  \n",
       "2                 0.7002         1  \n",
       "3                 0.5836         1  \n",
       "4                 0.6624         1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train, df_test = train_test_split(df, test_size=0.50, random_state=34)\n",
    "#df_test, df_val = train_test_split(df_test, test_size=0.50, random_state=17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_name = \"decision\"\n",
    "\n",
    "y_train = df_train[target_name].values\n",
    "x_train = df_train.drop(columns=[target_name]).values\n",
    "\n",
    "y_test = df_test[target_name].values\n",
    "x_test = df_test.drop(columns=[target_name]).values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = df.drop(columns=[target_name]).columns.tolist()\n",
    "\n",
    "lgb_train = lgb.Dataset(x_train, y_train, feature_name = features)\n",
    "lgb_test = lgb.Dataset(x_test, y_test, feature_name = features)\n",
    "#lgb_val = lgb.Dataset(x_val, y_val, feature_name = features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'task': 'train'\n",
    "    , 'boosting_type': 'gbdt'\n",
    "    , 'objective': 'multiclass'\n",
    "    , 'num_class': 2\n",
    "    , 'metric': 'multi_logloss'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's multi_logloss: 0.608604\n",
      "Training until validation scores don't improve for 15 rounds\n",
      "[2]\tvalid_0's multi_logloss: 0.531756\n",
      "[3]\tvalid_0's multi_logloss: 0.467911\n",
      "[4]\tvalid_0's multi_logloss: 0.419189\n",
      "[5]\tvalid_0's multi_logloss: 0.372591\n",
      "[6]\tvalid_0's multi_logloss: 0.337312\n",
      "[7]\tvalid_0's multi_logloss: 0.30683\n",
      "[8]\tvalid_0's multi_logloss: 0.275802\n",
      "[9]\tvalid_0's multi_logloss: 0.249638\n",
      "[10]\tvalid_0's multi_logloss: 0.225466\n",
      "[11]\tvalid_0's multi_logloss: 0.208234\n",
      "[12]\tvalid_0's multi_logloss: 0.189504\n",
      "[13]\tvalid_0's multi_logloss: 0.172336\n",
      "[14]\tvalid_0's multi_logloss: 0.16081\n",
      "[15]\tvalid_0's multi_logloss: 0.147741\n",
      "[16]\tvalid_0's multi_logloss: 0.135219\n",
      "[17]\tvalid_0's multi_logloss: 0.127531\n",
      "[18]\tvalid_0's multi_logloss: 0.117658\n",
      "[19]\tvalid_0's multi_logloss: 0.111598\n",
      "[20]\tvalid_0's multi_logloss: 0.103025\n",
      "[21]\tvalid_0's multi_logloss: 0.0959946\n",
      "[22]\tvalid_0's multi_logloss: 0.0923286\n",
      "[23]\tvalid_0's multi_logloss: 0.0857837\n",
      "[24]\tvalid_0's multi_logloss: 0.0801744\n",
      "[25]\tvalid_0's multi_logloss: 0.0779818\n",
      "[26]\tvalid_0's multi_logloss: 0.0729299\n",
      "[27]\tvalid_0's multi_logloss: 0.0689614\n",
      "[28]\tvalid_0's multi_logloss: 0.0672976\n",
      "[29]\tvalid_0's multi_logloss: 0.0636395\n",
      "[30]\tvalid_0's multi_logloss: 0.0599889\n",
      "[31]\tvalid_0's multi_logloss: 0.0573384\n",
      "[32]\tvalid_0's multi_logloss: 0.0543557\n",
      "[33]\tvalid_0's multi_logloss: 0.0542383\n",
      "[34]\tvalid_0's multi_logloss: 0.0518464\n",
      "[35]\tvalid_0's multi_logloss: 0.052098\n",
      "[36]\tvalid_0's multi_logloss: 0.0498192\n",
      "[37]\tvalid_0's multi_logloss: 0.0482391\n",
      "[38]\tvalid_0's multi_logloss: 0.0488723\n",
      "[39]\tvalid_0's multi_logloss: 0.0469185\n",
      "[40]\tvalid_0's multi_logloss: 0.0453569\n",
      "[41]\tvalid_0's multi_logloss: 0.0456112\n",
      "[42]\tvalid_0's multi_logloss: 0.0440956\n",
      "[43]\tvalid_0's multi_logloss: 0.0431494\n",
      "[44]\tvalid_0's multi_logloss: 0.0442237\n",
      "[45]\tvalid_0's multi_logloss: 0.0428682\n",
      "[46]\tvalid_0's multi_logloss: 0.0418239\n",
      "[47]\tvalid_0's multi_logloss: 0.0430162\n",
      "[48]\tvalid_0's multi_logloss: 0.0419482\n",
      "[49]\tvalid_0's multi_logloss: 0.0413624\n",
      "[50]\tvalid_0's multi_logloss: 0.042026\n",
      "[51]\tvalid_0's multi_logloss: 0.0410457\n",
      "[52]\tvalid_0's multi_logloss: 0.0403287\n",
      "[53]\tvalid_0's multi_logloss: 0.0416834\n",
      "[54]\tvalid_0's multi_logloss: 0.0413042\n",
      "[55]\tvalid_0's multi_logloss: 0.0405588\n",
      "[56]\tvalid_0's multi_logloss: 0.040011\n",
      "[57]\tvalid_0's multi_logloss: 0.041407\n",
      "[58]\tvalid_0's multi_logloss: 0.0407673\n",
      "[59]\tvalid_0's multi_logloss: 0.0405381\n",
      "[60]\tvalid_0's multi_logloss: 0.0413558\n",
      "[61]\tvalid_0's multi_logloss: 0.0407339\n",
      "[62]\tvalid_0's multi_logloss: 0.0403498\n",
      "[63]\tvalid_0's multi_logloss: 0.0417914\n",
      "[64]\tvalid_0's multi_logloss: 0.0413038\n",
      "[65]\tvalid_0's multi_logloss: 0.0411793\n",
      "[66]\tvalid_0's multi_logloss: 0.0426528\n",
      "[67]\tvalid_0's multi_logloss: 0.0422023\n",
      "[68]\tvalid_0's multi_logloss: 0.0419053\n",
      "[69]\tvalid_0's multi_logloss: 0.043392\n",
      "[70]\tvalid_0's multi_logloss: 0.0430039\n",
      "[71]\tvalid_0's multi_logloss: 0.0429365\n",
      "Early stopping, best iteration is:\n",
      "[56]\tvalid_0's multi_logloss: 0.040011\n"
     ]
    }
   ],
   "source": [
    "gbm = lgb.train(params, lgb_train, num_boost_round=250, early_stopping_rounds = 15 , valid_sets=[lgb_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lightgbm.basic.Booster at 0x177ed310c88>"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ref: https://github.com/serengil/deepface/blob/master/models/face-recognition-ensemble-model.txt\n",
    "gbm.save_model(\"face-recognition-ensemble-model.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = gbm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:  98.57 %\n"
     ]
    }
   ],
   "source": [
    "prediction_classes = []\n",
    "classified = 0\n",
    "\n",
    "index = 0\n",
    "for prediction in predictions:\n",
    "    prediction_class = np.argmax(prediction)\n",
    "    prediction_classes.append(prediction_class)\n",
    "    \n",
    "    actual = y_test[index]\n",
    "    \n",
    "    #print(\"prediction is \",prediction_class,\" whereas actual is \",actual)\n",
    "    if actual == prediction_class:\n",
    "        classified = classified + 1\n",
    "    \n",
    "    index = index + 1\n",
    "\n",
    "#print(classified,\" instances are classified in \",len(predictions),\" instances\") \n",
    "print(\"accuracy: \",round(100*classified/len(predictions),2),\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, prediction_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[67,  1],\n",
       "       [ 1, 71]], dtype=int64)"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "tn, fp, fn, tp = cm.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(67, 1, 1, 71)"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tn, fp, fn, tp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "recall = tp / (tp + fn)\n",
    "precision = tp / (tp + fp)\n",
    "accuracy = (tp + tn)/(tn + fp +  fn + tp)\n",
    "f1 = 2 * (precision * recall) / (precision + recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision:  98.61111111111111 %\n",
      "Recall:  98.61111111111111 %\n",
      "F1 score  98.61111111111111 %\n",
      "Accuracy:  98.57142857142858 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Precision: \", 100*precision,\"%\")\n",
    "print(\"Recall: \", 100*recall,\"%\")\n",
    "print(\"F1 score \",100*f1, \"%\")\n",
    "print(\"Accuracy: \", 100*accuracy,\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ensemble method is more successful than human beings!\n"
     ]
    }
   ],
   "source": [
    "human_limit = 97.53\n",
    "\n",
    "if 100*accuracy > human_limit:\n",
    "    print(\"ensemble method is more successful than human beings!\")\n",
    "else:\n",
    "    print(\"human beings are still more successful than the ensemble of face recognition models\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpretability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 504x504 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "ax = lgb.plot_importance(gbm, max_num_features=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"PATH\"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TbXbJbbfdZi/funVr0EyTUGU6oTz00EPs2LEDwG/C06xZs6LeMLdFixbs3LkzqvdRmVJTU+3fyj0prnHjxn4T5Mx+de/vaGUNAQwdOtSe/u677wD/zCH338B93bIoTbZZnz59ynQ9k4kzePBgv6bPxpgxY/xeByYzKnAfjxkzJuK1loXX67XrNFlK06dPj+p9RtPu3bsBaNmyZbm3pZ5DIiIilWDatGls3bo11ssQETnnjR07NiqlKW5er5fevXsDTtlXgwYNAGjevHnQ8hR3Wc/mzZtLLG0KvC/TJ2XevHlRn6o0f/58HnvsMcDpcVSzZtXOJzABHvfEqz179vhNinNfFoy7fMzdc2fbtm0cO3YM8O855N6++7kAzt8XiPhvnJaWxueffw7AzJkz7flTpkyx5WTu0kWA5ORke9qUIlYks/2UlBT7eODMPnD3xzJK6gNUWFjI2LFjASdYunjxYgAGDBhA+/btAaekbP78+YB/oMzN5/PZ67uFe12ZgNCqVatscDApKYmJEyeGva+q7ptvvqF79+4AfPXVV/Z0EJpWJiIiIiIiIiIi4SlzSEREpBK4f1EL1qxURETKZ+nSpYBTlvLPf/4z6vc3YcIEwMnmMZlKoaY3paWl2dKmpKSkEkub4uLi2L9/v72O4c5SidZx3P79+2nbtq1d0y233BKV+6kooZotu/ePKWFKS0sjJSUFcEoDp06dChSfOGf2sTu7ZO7cuRQUFAAwderUoNkr4dYQjslUGjlypH0uTZo0ya8syy3amUOGe8oeYLN93JP4jJLKyuBMlteyZcvs6wGc0j+AhISEkFk8JTXVdguchJaYmAjAwIEDbTZWabL3qqpHH32Uf//73wB88skn4a4a0c5TcEhERKQSxMXF2YOVaNfTi4hUR3feeac9XRnBITMeu3///vbgNrD8xyhtaVNcXBxJSUl2m+Z8n89ng2Dhysp8Pp+dyvSzn/2MAQMGAJH3JXniiScAJ9i1fv16AGrXrh3RbUUkukyws3v37qSnpwMwYsSIcDdRWZmIiIiIiIiIiIRXtbuLiYiIiIiIVEGm8bTH42HgwIFhr9uiRQu2bdsGRFbaBNhyJveErUinlWVlZdnmxnFxcbbMpn379natAwcOpH///gBceumlfrefMmWKve+HHnoIgL/+9a8l3q+IRNfx48e5/fbbAbj++utLyhgqFZWViYiIVAKVlYmIRFdll5UVFhYCTiDFlHBVFatWreKGG24IepmZPlZUVMSpU6cAaNKkCddeey3gBI1MGdqJEyfsdp588kmmTZsW7aWLSBAnT54EYNSoUXz22WcAfPbZZ5FOWlNZmYiIiIiIiIiIhKeyMhERERERkVJasmQJUDUnUMbHx4e8zGQguO3fv5+33noLgHfeeYcTJ04AThPqDh06AM7krs2bNwPw6quv2gwkEYmuH3/8kdtuuw2Azz//nNWrVwNEmjUUMb2iRURERERESpCcnGx7BQF+08SqmtatW5f6NqdPn/b7F5z+Jjk5Ofb/ZjLSrl27eP311wG44IILyrNUEQlh06ZNAIwcOdKWgH744Yf06NEjKvensjIRERERERERkWpMmUMiIiIiIiIlcE8TS01NjUnGUH5+Pjt37gRg+/bt7Nixw+//5nRubq4t+wpWRlaSWrVqAU5DajPdLDk5mebNmwOQkJDAFVdcAcBLL73E8OHDy/GoRMQwmXsvvvgiycnJAPTu3Zs33ngDgGbNmkXtvhUcEhERERERKcG9995bIQGhkydPsnv3bgB8Ph+7du0CnIBObm4uADt37mT79u0AfgGgY8eO+W2rcePGtGnTBnD6DJlysr59+/LKK68AsGfPnojWZQJCJ0+eZNiwYQA8/vjj9O3bt9h1169fz8SJEwEYMWKE7Yfywgsv0KpVq4juT0T8ffHFF9x///0AeL1eHn30UQCeeOIJateuHfX7V1mZiIiIiIiIiEg1pswhERERERGRUjpy5AjglHOZ7B9T6gVO9o/P57Onzfm7d+/2a/ocFxcHwEUXXWSzgNq0aUPPnj0BGDp0KG3btrXnm+ygdu3a0aBBg5Dr+/DDD4HwmUMmW+jUqVN26tq0adPo3r172MfevHlz25z6rrvu4ne/+x0Al112GQ8//DAADz74II0bNw67HZHq7vvvv+eZZ54B4G9/+xv9+vUDnCyiyy67rFLXouCQiIiIiIhUawUFBTbA4/P5bBnXjh07bHnXrl27/Eq99u3bV2w7tWvXtsGb+Ph4O1L+5z//ue1Z1Lp1axsEatu2LS1btrS3rUgdO3YE4KOPPrKTjuBMQAjgnnvuAeCxxx6z1y+tYcOG8dVXXwHw/PPPM2fOHADmzJnDH/7wBwAmTZqkQJHI//n+++95+umnAfj73/9ug7+vvvoqY8eOBc4EjSuTyspERERERERERKoxZQ6JiIiIiMg54+jRo2RnZwNOOZdp8pybm8uuXbv8Sr3MZYGNnhs2bAg4mT0mE6hNmzZcddVVgJP9Y7KC4uPjbRNmkwVUFZh1nzp1yk4uq1WrFr///e8BePjhhytsvfXr1wfgySefZNKkSQD8+c9/5oUXXgCcLKK77roLgPvvv59LL720Qu5XpKorKioCYPXq1bZJ/BtvvGEzCVNTU/nNb34DYF+nsRJnFhtjVWIRIiIi0RIXF8c///lPAMaMGRPj1YiInHvuvPNOAJYvX86hQ4fs+SYA0qpVK79Sr9atWwft5dO2bVsbHDqbLVu2DIBbbrmFGTNmAPDAAw9wwQUXVNoaDhw4ADgHwKmpqQBs2bKFgQMHAk6g6Fe/+hUAderUqbR1iUTTjz/+CDglYyYg9O2333LNNdcAMGHCBG6//Xag0gJCEdWoqaxMRERERERERKQaU+aQiIhIJVDmkIhIdJnMoX379vHSSy8BTnZQRTd6lrIxx51ZWVnMmzcPcLKbzj//fMDJcBo1ahRDhgwBKr5Bt0i0HDhwgOXLlwOwdOlSVq5cCUDdunVtyVhiYiI9evSI1RIjyhxSzyERERERETlnXHDBBXTo0CHWy5AAZvrS4MGDGTx4MAB79uxh6dKlgHNQ7fF47FSzhIQEbr31VgAGDRpk+xqJxFJ+fj4AK1asICMjA4B3332X885zirKGDRvGa6+9BsDIkSNp0KBBbBZaBiorExERERERERGpxpQ5JCIiIiIiIpXuoosustPTfv/737N7927eeOMNwMkkuuWWWwCnaa9p5nvjjTdy4403AtC7d2+bkSRSkY4fPw7AmjVreO+99wAnQ+iLL74AnJKxoUOHAvDaa69x8803A5zVzezVc0hERKQSqOeQiEh0mZ5DgH2/lbNbYWEhAKtWrbIH6CtXruSHH34AoEWLFnbyWb9+/ejXrx8AV111laafScQOHjzIxx9/DMDatWtZu3Yta9asAeDw4cP07t0bgCFDhtiA0DXXXEPdunVjs+DSU88hERGRc112djYAb731FikpKcUuT0pKYsSIEQD2S/PZrLy/EFeRH8UEmD17NgCTJ09m1qxZADzyyCOxXFKVkp6ezq9//Wv7/8WLFwNwxx13lGubixYtAiAzM5PExETAaZTaq1evkLfzer0AZGRk+L3PmNuPHj2aQYMGFbudz+ejffv2Ea9Pr08JZPoP3Xrrrbb/EMCWLVsAJ1D04YcfAvCnP/2JHTt2AFCnTh2uuuoqAPr370+fPn0A6NmzJ126dAGgRo0alfMgJOaOHTsGwDfffMOGDRsA+Pjjj21A6KuvvuL06dMAXHLJJfTv3982TR8yZAgXXXRRDFZd+dRzSERERERERESkGlPmkIiIyFkqOTnZ/oqfmprK/v37AeeXVpOKv2LFCvr37w84WUQzZsyIzWIrSFFRkX1sTZo0KXaZW05ODgALFy4MmlUlEg1er5dVq1YBZcuESk5OBiAlJYXNmzfb87t16wbA119/XerXsXubbuaX8Xnz5rFu3TqgeIZhVlaWnSwF2F/de/XqRVZWFuBMnwqW2WQyjkpiMsdEImWyf7p06WJ7FoGTrQZOnxjznP7www+ZO3cu4PSRMVPPunfvbsuFevbsyeWXXw44r7WWLVtWzgORcjMZP9u2bbPvmV6v177/fPnll/b8kydP2p5AV111le0T9PTTT9vvShdeeGGlrr8qUc8hERGRSlCRPYfcB3qRlOO4y3fOhQCREVhiFuo7TWFhoQ0kVZHvPXKOyc7OZsGCBQAMHDiQAQMGANCuXbtSbcfr9dqDVfB/vrqf7+4ATSRre+uttwDnPcCU6QSWrXk8HgCWL1/ud/uRI0eSmZkZdE3B1ua+PC0tzfbnCLUv0tPTufTSSyN+POGo55CEcuLECcApK9q4cSPgH0Dwer3k5eXZ6zdq1AiAzp0707VrV3vaBGkvvvhi+5xu2bKlStSi4KeffrJlgj6fz5YSbtmyxZ7Oycnhv//9r72+0bFjR/t+0rNnT3u6V69edOrUCSh/mfpZJqIHq7IyEREREREREZFqTJlDIiIilaCiMoeys7Nt6jNgU6XNL5vBmPIq84unu3wk1C9nJjOhd+/eNqNg0qRJQZvOAvYX14ULFzJ58mTAyUQwWUrujIBQ97l582YWLlwIOFlRptnt9OnTadGiRbHrh8sciouLiyhLyOyb1atX2+yIzMxM+5jHjBkTsgGwKW9bsmQJ9913nz3fZHONHTs26Lrz8vLs45w8ebK9rxkzZpQ7cyLUOpcsWQJQbJ1jx44FKLZO8/dctmyZ321SU1MBSEhIiOixufeFaehZVFQU8jkQ7jLzXHeXCSYmJjJ9+vSwjyGS52RpFRYW2ia4b7/9NsOHD+e6664DzjTQLYu0tDS//R0qc8j8He69994St+nz+exEncB9FGxfB75uAjOH3n//fQAGDRrkV+JpXq8vv/xyiWtymzBhQqlvE4oyh6Q8zHvGpk2b2Lp1K+B8RpjT7owV0+AYoGbNmvb9rX379rRp0waANm3a2Oyi5s2b07RpU5o1awY4r8WmTZsCcP7550f7ocXMvn37yM/PB2Dv3r0UFBTY07t37wYgNzeX7du329M7d+4EsJcbJgPYnc3VpUsXv9Pme47J/BIroswhBYdEREQqQUUFh9x9hqB0ZVLmQDApKQnAr7ws8CDRBJCAEoNRhYWFNsiQmZnJtm3bAPymFG3evNnvNsEOSjds2ECHDh0A/35CoUrhggWH3IGwkvaNu5eKe32BE5ZCle6NHDkScB6z2V+dOnWyvS0KCgr8DnrNgfTYsWPtwfa2bduK7ScIH+wrLffB/bp162xK/dy5c+0X9cB1uv+ewfreeDweGwRyB0Nmz55tAzF79uyx5z/55JO2v435u5QUmAi83AQsO3ToUOz5Af7P57I8J0vi8/lYu3Yt4AQTx40bB1TsFMAJEybY/QSh90eoErDSiiTgFFjq5r7+559/DsCOHTuYP38+UDwAFYp5rX7++eflmsDmpuCQRJt5Tebm5tr+Rjt27CA3NxeA7du329M7d+607z0FBQV+ZU9udevWtYGiZs2a2Z5IDRo0sO919erVs+c3adLEnq5Tp47dTpMmTYK+r9avX9/vesaRI0eCrunEiRMcOnTI/v/w4cMAHD16lAMHDgDO6PejR4/a6x04cMBer6CggL179wJOj59g6tevT6tWrQBo3bq1fW9u06aNDa61a9fO73Sk7y0SlMrKREREREREREQkPGUOiYiIVIKKyhyKtAlzaW8b6jJ3M2cInl0Q2Ng2WGbIrFmz/DJvSps1UlIT3GBK2jfu2y9fvtxmY0SyvlCPGbATnD744AO/bJb09HSAYrcL3E9QtilXgULdn3udH3zwAUCxdYa6jXutwaZThdqn7myssmYOhbss8PKyPCdLEhcXV6F/n1D34Vaax1xaeXl5thQmVCaYYbJ8Jk+e7FdiZqxbt67UGVRpaWkADB06tNSNu0NR5pBUZQcPHrTZmvn5+X4lVu7TJgPnyJEj7Nu3D3Cydo4ePQrA/v377XWOHz9up3WZ7NRg9xssg6dOnTo2A8ntvPPO83sfMNepV6+ePf/888+nXr16dvJXw4YNbXmcu3yuWbNmNG/evNj59erVC7uvpMKprExERKSqOFuDQ8EuC7w8sCdJKKV2/A1wAAAgAElEQVTZZlmCQ6UtK3MHENwHt4FlZcHWEK78JxR3GVpJKuL72YQJEwDKtM5Q06lKKm1yl5XBmZKviRMnlrrfTWU/J0tSGWVllRkccj//N2zYELYHkznonD9/Pvv37wfwK28FbPlMpIEe8/ysqH5DoOCQiEgIKisTEREREREREZHwlDkkIiJSCc62htQVneUTai3l2WZFTCszvF6vzbDp3LmzX/ZLWddX0nojvV1ZlfX+ypu9kpycDPhnlng8nmINi6OZOVSeDLtIRGtaWXmytiLl9XoBZxKhafIdLmsoJyfHvh6mTZtmM6UCS/eCva+Ekp2dzZEjRwBCTkAsC2UOiYgEpbIyERGRqqKqjrJ3r8+tPAfipuwk3EFytINDkSgsLLQ9ZFJSUvx66JS2bMlM5go3TSXYNvfv31+uYEI4wcrY9uzZU+LEl4oKUAQGDwJHnVdmcCiS52R5ZGdns2DBAgAGDhzIgAEDgMhLrIxojLJ383q9Nng3f/78iKb/uEsoA19jZQ1AJicn24BTRf5NFBwSEQlKZWUiIiIiIiIiIhJezVgvQERERCLXr18/W76RkpJiMzzCTU9yZ4EkJSWVq4GuyVgIPM+d7fDDDz8A4UtVqoL58+f7lT65p26VxOPx+O3XXbt2AeEzh8y+C9xX0dpPJrMncJ0lZYuMGTMmombOwTLg4uLibGPiO+64gwYNGgBONpLJPqnIBsShVPZzsl+/fvZ15fV6Wbp0KVD6qWZ9+vQp8/WysrIYPHgwAO+//75fuZYpJcvIyChW3meYptNLlizxy0hyNzQPx2SGhWPuo0mTJlHL4qoseXl5djLhokWLbAZdZmamzdrzeDy8+OKLQPEsMrMvVqxY4ZdhZ94nEhISIsrsEhGpKAoOiYhITMyePRugWG+XYKOiAycguafi5OXlAbBw4UJ7HY/Hw6RJk4Di/Szc9+v+Em5GOleRcuuw3D09zGNu1KgRt912G+CUabgPPMx1kpKSIuoHAmfGoF966aV+5w8cOLDYdYcOHer3f3cQwByUbtq0qVTBl3BCjestLCws1QFn4HPPPJfMAV84CQkJNoCSmZlJRkYG4AQfsrOzAdi4caPfQXbgfgJnX5lgidfrZdOmTUDpAlXh1mjWZ9aakZFhAyTZ2dls3LgR8C9Puummm/wCS6Ys0c3j8XDTTTcFvV8zonzixIn07dvXnm9e25Whsp+Tbr169SpzEKpXr15+wd9g+z4pKSno9k1gyJw272XuoJHZbjim7NRYvny5DXZkZWXZ91TzPDciCQ6ZPk1Dhgwp8bpV3fjx4/2CqGZ/eDwe+xnVvn172rRpAxQPio4dO9Ze3/yt8vLyGD9+POC89hYuXAhErxxSKtf+/fvJz88Hio+vd58+dOgQ4IyyP3DgAACHDh2yo+wPHjxozz916pQ9/9ixYyHvN9h3m1q1atnx84HM+bVq1aJOnTqAM9K+SZMm9nS9evXs/xs0aBB0lL37dPPmze1p88OBVC0qKxMRERERERERqcbUkFpERGLK3WA5MTExZMmJ+eXa3UTV/SvrmDFjbBaA+5fyDRs22F/ZZ8+ezejRowEn68hkoMyaNcv+mh6tz8WKakgdyGRBrFq1KuiErVmzZtlf6cNlMwQ2ljVTjEzzWoBJkyaFnCxkMhzmzJljszRCZXAFa2JrmP0f7jqRiOTv6G7Om5mZaTPJhg4dysqVKwH/ErDly5fbjBoAn88HOJky5vkTLmvNyMnJYc6cOYCT0WK2GW7/lofP57PZPCkpKRHdn8miWrZsmd8+KKnkJS4uzjbndt921qxZftmA5fn7FhUVhby9++9emudkVRTY0NvdMD2YUGVlpd3XwV47Jptu6dKlfmVmJhvM4/GEbYpvmNdbpBmMpVXZDand+7akpvnuy91/q8Am8SYDqX///iX+zaXynT59GoDc3Fy2b98OwPbt28nNzS122n2d/Px8Tp48GXSb9evXp2nTpoCTaWMycOrXr2+zxho0aEC9evUAJ1O4YcOGANSsWZPatWvb6wTTsGFDatYsXjB07Ngxm3UUyHw/On36tM1IOnr0KPv27QOcrKajR4/a6x06dIjDhw8DUFBQwN69ewE4fvx40O3XrVvXZmy3bdvWll22bt2atm3bAs73tNatWwPQoUMHlVmWj6aViYjI2cFd6uUuGTNCldu4D55CTdFxl1K5D1zdXzLy8vKiXlYWreBQRYn26G8RkWg7W4JD4SbAufsylTQRUCqWCeps2bKFLVu22NNbt24FnIDzf//7XwB++ukne7tatWrRsmVLwPnuYoIbbdq0sadNSZX57tG0aVMbEKpfv360H1rMHDhwwJbM5efn26DR3r172b17N+Dsd/NjS25uLjt27ABg9+7dfq+PRo0aAdC5c2cbjO7cuTNdunQBnKmt5vwLL7ww2g/tbKNpZSIiIiIiIiIiEp4aUouISMy5m5Oach53g9xVq1bZcjC3RYsW2dOhSidSUlJs5lBiYqLNEFq8eLFtqNuiRQtlyohIWGUphdP7igQTbgKcu/l0JFMDJTKmvOnrr7/myy+/BJzyadOU/4svvuDHH3+01zeNlrt06WIzU0aNGkW3bt0A6NSpk81wbtmyJeedp5yLYBo1amQzfjp16lSq2x4/ftxmEfl8Pr8MLnN6+fLltoTY3ZC7Xbt29OzZE3BK6nv37g1Az5496dy5M4D+ZkEoOCQiIjFneuEkJibaHiVm8hbA1q1bi40BBv8vzpEchD300EP2i4a7l0dgPxQRkUAK9EhF8Xg89vMrLy8vZC+VSCbAiT9TnrRmzRo7eW/t2rU2IHTixAnb06dHjx72+8fo0aPp0aMHAN26dbNTtSR2ateuTceOHQHo2LFj0GmpcOa9OTc3l82bNwNOOwLTO/HNN9/kueeeA/z//ldffTXXXHMN4PT46tOnD+CUAFZXCpeJiIiIiIiIiFRjVTpzaPfu3Tz00EMAnDp1KsarERGp3saOHes3qSkaEhMTbbr9ihUr7OSNcePGlXjbnJycEqfldO3a1Tb29Hq99r7cU76qWwZRqFIZc76yJWKnvBO9RKRqGjNmjM0c+u677/wyh0xDaiBoObU4vv32WwDeffdd/v3vfwPw8ccfs3PnTgDq1avH1VdfDTil61OmTAFUVnQuMp+Vbdu2tQ3A3e0KIHhZ4fr163nzzTcBmDlzpo03dO3alf79+/OLX/wCcCaYmobj57oqPa1s0aJFduqA3hxFRGJj6dKlgPNltjKmv0yYMAHwH/EdalJLWlqaLUNLSkqyQZ7GjRvbUdwLFy60AZ+4uDj2799vr2N4vV5bj15dp5WJiJztKnNamXvKJeD32eKeOGa4R9YXFhYyduxYe9n8+fMBp/9deno6AKtXr+bll1+O6mM4G5g+QB988AHvvPMOAO+9956dbNqyZUuuvfZaAAYMGED//v0BuPLKK6lVq1YMVixno4MHD/Lxxx8DThni+vXrWb16NQCHDx+2JYfDhg2zgafrrruOunXrxmbBpadpZSIiIiIiIiIiEt5ZkzlURdYpIlLtVOYvsQDZ2dmA0xwwNTUV8J9c5hb4y20w27Zts82s4+LiSEpKsts05/t8Ppsh9dBDD1GnTh3AmbJx55132l8ir732WuLj48v0uJQ5JCISXZX5eRWq7LOoqCjsZYbJbl22bJnNgAVnkibATTfd5JfhWl3s3LmTN954A4CMjAzWrFkDQK1atfj5z38OwI033sjQoUMBuPzyy8tVgisSysmTJwEnk8hM0n3vvff47LPPAKhbty433ngj4FQ53XzzzQB2OlsVE9GLRMEhEREJq7KDQ8bIkSOZNWsWQNheQmYySVpaGikpKYDTu2jq1KkAflPO4uLi2LNnD+CUm5kytMBpZe4vmrVr1+bEiROA81lk6s6vv/56BgwYAMDPf/5zOzK1Ro0aQdep4JCISHTF6vNKym7nzp32x5mMjAzWrl1ry/ESEhK49dZbAfjFL35B/fr1Y7ZOEaOgoACAd955h4yMDAAbPAKnR9GoUaMAuOWWW+x0tBhTWZmIiIiIiIiIiISnzCEREQmrsn+JNY08p0yZErNmnJ06dQLg+++/D3mdGjVq2AyjkydPUq9ePQD69u3LddddBzjNMU12UaNGjZQ5JCISRebzat++fbz00ksAtG7dmtq1a8dyWfJ/Tp8+DcCqVat45ZVXAMjMzLRlOL/85S8ZNWoUgwYNAlBDaTlrHDhwwE4+W7p0qc0kqlWrln1fuv/+++nVq1eslhhR5lCVHmUvIiLVz5IlS4DYTqls3749ED44ZEaeGkePHgWcCTNr164FzoxONfRDh4hI9H300Ud07NjR/t+UA7dq1Yr4+HjbO65169Z29HWbNm1o3bo14IzEbtiwYSWvuuKZHyR+85vfMGPGDAAeeOABLrjggkpbg/nB55VXXrF9BL/77juuv/56wCnx/uUvfwmgIJ6ctRo1amR/+BszZoydXvj3v//dBkJffvll+vXrB8Dvfvc7fv3rXwNQs2bVCcmorExEREREREREpBqrOmEqERGplpKTk20jacBvmlhlMr/y5ObmUrduXcD5NcdMq4hUUVFRsYwhQxNVRESi74YbbuCxxx4DnIbHubm5gPP+vmvXLr7++mvAmTxkLjt27JjfNkzmUNu2bW1GUZs2bWymUevWrW0GUnx8PK1atQLOZClVBZs2bbKnp0+fDsAzzzzD73//ewAefvjhqKx33759APz5z3/mz3/+sz3/7rvvBuC+++6jW7duFX6/IlWFaar+4IMP8uCDDwJORuO8efMAGD9+PE8++SQAjz/+OL/97W+B2JdSKjgkIiIx5Z4mlpqaWiFBof3797Njxw7AmWZmvvzv2LHDTjfLzc2152/fvp1Dhw7Z25uJY2UJDtWoUcNOpnj44YeZOHEiABdeeGE5HpGIiESqXr16tnwjUgUFBezatQtwPjd27twJOJ8b27dvB2DXrl12jPWOHTtsEMStdu3aNpjkLmFr1aqV/bxr3bo1bdq0AZzgkwnQVHRZlXkMNWrUsJ9lJ0+e5IUXXgDghRde4J577gHgscce8yvFK63Dhw8D8Nxzz9mAUI0aNXj44YcB+MMf/lBVR3yLVIprr72Wa6+9FoCnn36aZ555BnBKzJ5++mnA+cF03LhxAJx3XuUXeamsTERERERERESkGlPmkIiIxNS9994bMlvIlHoFZvyUlBXkzgICbCZPu3bt/MoAzCSx+Ph4+ytuu3btyMrKArC/eIZiysTOO+882+BzypQp3H///X73KyIiVVuzZs1o1qwZAJdffnlEtzly5AjgZJ+arKPt27fbz6idO3faz6X//Oc/9vzdu3fbyV1w5rPkoosusp9F7hK2Vq1aBW2c3a5dOxo0aBByfWaoQuAAhRMnTtjTr776KuBk7t5xxx0ATJs2je7du0e0DwDefvttHnjgAcD53H700UcBmDhxorKFRILo0KGDbdD++OOP8+yzzwJOyaV5Tc6bN48ePXpU6roUHBIRkUpRWFhoAzk+n89+Sc7NzbVfnnfs2OF3ncAgDzgBF5Oa36ZNGxvsGTBggP1SHR8fb68THx9P48aNS7VWU0IQrKTMfImPi4uzPSamTZtmeymYfkUiInJuq1+/PgDdunUrVQ+dkydPsnv3bsD5rDOBJXe5886dO/nyyy8BWLFihS0RC+yN1LhxY7/PPnfgyN1zKBR3oGjp0qUALF68mJtvvhlwDlz79u1b7Hb5+fm2bPr111+3k5fmzJnDRRddVOL9ioijffv2thfRhAkTuO+++wC46qqreOSRRwB46qmnKmWan8rKRERERERERESqMWUOiYhIqZWUBRQsKyhcqZf7V8/+/fsD/llB7uuYCRDRZO7XME0Bi4qKbMPOJ554gjFjxgCxny4hIiJnj5o1a/qVOJdGfn6+zSIyJWzu/5vTn3zyCXv37i3Vtt1ZRO+88w4AmZmZDBw4EHCa5ZrSu4SEBPvZuGLFCoYNG1aq+6pIeXl5AGRlZbFo0SIAli9fHpO1ZGdns2DBAsApC0pMTAQgMTGRXr16xWRNcvbo1asX69atA+Cll17i8ccfB5yy1IyMDABatGgRtftXcEhERCLyxRdf2NG+oQI9bdu29ZvO0q9fP78vwO5ysMoI8pRV4Jf1Sy+9FIAnn3ySW2+9FYjNFAkREanemjdvTvPmzQHCBhsOHDhQ6pJqN3egaPXq1fZfU9py/fXXk56eDmB77sWKGQluSnNiwfQqHDx4MNu2bQPg5ZdftvsoOTk5ZMDK5/PZnjPugNLo0aMZNGhQtJceVnp6ui0ZBKfkELD9qSqCz+dj7dq1gPMcM3/HoqKiYmsBWLRoEZmZmYATdDP7K9zrwQQQly1bZsu2PB6P/ZGvIh9PeZnvl7///e8ZMmQIACNHjqRPnz6A8xiiFWjUN1sRERERERERkWpMmUMiIhKRli1b8tBDDwFOZo2ZnBIfH1+ls4DKwjye6dOnc9VVVzF8+HDgTDNqERGRqsyUl5WGyVioUaOGzRyqXbs2HTp0AGDLli2MGzcOcLJiatSoUTGLLaeXX34ZiG3mkGnmDdgsaTiTkRIsM6WwsBAAr9drH8PMmTNZsWIF4GQhmWwjj8cTnYWHkJycDEBKSgqbN2+255vG619//TUzZswo132kpaUBzoSuWbNmAU4m0MyZM4OuJyUlpdj58+bNs3/3devW0a9fv2LXycvLY/z48YBTJmkyu7xeLyNHjqywxxMNl1xyCQAff/yxfQ4NHDjQZvNVdAZRXGC6VowEXcSiRYu48847nStUjXWKiFQ75n0Y4J///GcMV3J2i4uLs/vPpDGLiEjF0efVGatWreKGG24IelnNmk5+QFFRkR1z36RJE6699lrAOfgcMGAAAD/99BM33ngjADNmzOCPf/xjtJdeZu4fcCr72LEs921Ko0IFfmL1eLxeL7179w563+41bdiwoczBCXewJ9x2srOzAXjrrbeYPHky4EzoMyVm7pI3j8cTtHQvLS3NlpK5H4/P56N9+/b2/Pfffx8g5qV8oZjX6u23327L8L744otIpwNG9OumyspERERERERERKoxlZWJiIiIiIicQ8zUUHCyPUy2RPv27e30sYEDB9oJoWbwgmEGT1x++eW24W9VzhoqiWlIvHDhQpuB4vF4mDRpEuCfLVJYWMiSJUsA/DJOkpKSmDhxIuBMjApVah5pxk8kpWJm3wcyZV9AhZdDrV+/PuLrlTZzyGT8pKSk2EydcNto3bo1ABMnTvRrsG5KrNyZQyYTK1Co893lf3CmNLCqZg6ZMs5FixbZNY4fPz7k4ysLBYdERERERETOIYMHD7Zldr/61a9smVjLli0juv3TTz8NOCU8ph/M2crdc2bMmDE2YJOVlcXgwYMB/9KmKVOm2D42e/bs4dixY4ATWCsoKACcPkehyq3KWwJmehEBtudhZfr8888jul5mZib33ntvxNvNy8vzC+Z88MEHgPNcNUGwcePG+fUNCgzghJOamhpynZEwf3PT/6mqql27tp0a16NHD9566y0ARowYUe5tq6xMRERERERERKQaU0NqEREJSw0+K4b7V8XRo0fHcCUiIucmUxYyZswYfV6Vw/79+2nTpg3gHI8lJCTEeEWRCZW9k56ebjNWAo8pzW2SkpJseVZycrJfhlBJ2y/pstLKysoC4IUXXmDhwoUAfiVV0RZYLhcqQyrwspK4/w7u27ozuMCZOgYEnTxmmDLBiy66yJbnLVy4MOh+mjBhgt8kuz179gChSwPPprjDY489xqpVq4ASM74iakhdLcrKIhk9HO4F7mZG3wWmuHm9XsCZDGDqWMGpJTVTaUw6JzhpiZE88cJtF5wPv8DtBns8JSnveOaz6UV0rps9ezYAkydPtmnAjzzySCyXVKUEfjCZtMxgI0bLa/bs2fZ1G+41UpY1uT8UQ9Hfv2qZOnUqW7dujfUyRETOWSbwHo3P9OokIyODOnXqABVTqhJrixYtsqdDHfOkpKTY4JC7h4/P5/MbU18ZXnjhBQCmTZtWqUGhaDPj1wP16NHD7/8LFiwAwgeHTAANzvy9Qu2rcePG+QWHvvrqK6B4b6GzsXzyrrvu4vnnnwfg66+/pnv37uXansrKRERERERERESqsWqROVRUVGQ7uqekpNjzly9fHrJLvPmVPz093UabX3zxxaBNsZKTk+12PR6PX0MzOJP98+yzz/pFLUsSbLtAsW0/++yzAKXadqCioiLb/KxJkybFLnPLyckBnNQ99/4UiSav12vTJsuSCeN+D9i8ebM9v1u3boATba+oaQ+m8Z0726+i1/Txxx+XuA6VLlUtzzzzTKyXICIiUqJ169bRt29fAGrWPPsPF90NiSOtdkhLS7O3NRklJX2vqwjp6en2+DRc5kw0eTyeiJo4RzJtzS3UsWqLFi2CXi9UY2iv12sz7gOPu4Pp16+fLVVbsGCBLWELzBS65pprSngEVc9ll13GBRdcAMCaNWvKnTlUbXoOBQt8eDweli9fHvZ2ycnJtgt7YGAoWMBpz549xZ7goW4T6jGVZbvmduY25dlfkdaSFhYW2v1ZRZ5Hco7Jzs4GnDfygQMH2hLK0kwuAOdDpHfv3vb/oWqngwVgSyvwvoLdZ0WsacKECQDMnDkz6inH6jkkIiJSfdx88800bdoUOFPiczYI1ffHff7mzZvp2rVr2O24y/23bdtmv3dGs+eQSSbIyMio8NH0UHILEfea09LSuO+++4Je5t5OampqqaaVjRw5MmSgzr1dE3QKPE43+yg5OZn58+cDxQNLpeHudZSYmFjlp5SFctlllwFOOe0TTzwR6moR9ZBRWZmIiIiIiIiISDV29ucJRsj8sr548WIbCc7MzLQRyMBf5X0+H+BkGgXLUvB6vX6ZPe+//z5QcvTSRFdDlWOVdbtm29Eu84qLi7NR3saNG4eMjJvSs9WrV9sIcWZmpl8j7VDNAk2W15IlS/yi1ib1b+zYsUH3R15enu3oP3nyZHtfM2bMKFcmSCiFhYUsWbIEoNg6x44dCxT/u5kmwsuWLfO7TWpqKgAJCQkRPTb3vjANiYuKikL+KhDuMlPO5C4TTExMZPr06WEfw8KFC216rcfjsb9ylGdfFxYW8uGHHwLw9ttvM3z4cKD82THr168v1fXK8hjMfjGlb9FcU15enk25nTdvHklJSQB07969zNlVVUVeXp5tMrho0SL7q1FmZiYjR44EnOfbiy++CBR/nOb9Y8WKFX5Nvkt6jYmIiIjj/PPP5/Dhw7FeRoVJTU2137vd318bN27s973WtC1wf3+ojO9TeXl59vtjYNaQOVadN29euTJbSpPN1KdPnzJdz3x/Gzx4sD2GdTd9HjNmjF/mkDneDtzHZpiTm9frJSMjA4D58+cH/S7nPjYLl9Fk9ungwYPt8aI57jkbHThwAICGDRuWe1vVJjhkBHYlNy/EwIPBlStXAqF7dpgnpxHYZT0U8+QP9QIt63bNtqNZ3mUCPiVxp+ht3rzZvjh9Pp+dppaZmcmOHTuA4v1jTGAlMzPT1od26tSJuXPnAvDkk0/6vTmag8Hx48fbN5xt27b53ZcJgJSURloaY8eOtfe3bt06OnXqBMDcuXN58sknAYqtc/z48cXWBGd6zGRmZgYdWen+IDPjFwF7P0ZJASIonlZ69OhRwAk6meDQvHnzaNasGeD/IRX4GMz0vvbt29t9EUm6rpvP52Pt2rWAE0wcN24cELrOuCxKGO1omcdQmhRZwzw/Z8yYEVFNennWFNhvKFhQOFxPtarM/TqGM6WFHo/H7/lmRuwGPk/M+4fH47HP+by8PL/nbSzGwopUtgMHDtj398OHD7N//37A+Sw4ePAgACdPnvS7zalTp+xtg6lRowaNGjUKeln9+vUBqFOnjp1yVL9+fdsLoV69etStW7c8D0lEKknPnj157bXXYr2MiJkAT+B5JoCQkJBgg0MpKSlBvzeZ7xjg33PH5/Nx7NixsNs3wQbDHDOV9H3YrNv93SfUd8jAEivThgSKB5TKq1evXvaHx5SUlKDHgElJScWOn93j6M1p97HpTTfdZL+bZmZm2u//bh6Px+943R1wMsIlQ5hjx0DBJoAnJSUxceJEoHzlabFUUFDAzp07gfL9QG+orExEREREREREpBqrNg2p3Upq+FxYWMiUKVOA0NkLkTZtLq1obbc8awhU0prctw/MXgi2bff23A3gAi8zkeMPPvjAL0Kenp4OUOx27vsyZVhlmXIVKNT9udf5wQcfABRbZ6jbuNe6ePFiAL+yu1D71J2NFSozKNx9lXRZ4OWhHkPgvi7Nfo6Li6vQv0+o+3ArzWOORHJyst8vD5FsszxrmjBhQkTTCYOl9JZFZTekLqmpY6jL3VmLgU38TQZS//79g77GRGLpp59+AmDnzp3k5uYCzi/K+fn5FBQUALB371727t0LOL8Uuk8fOnQIgCNHjtjTVZF57TZp0oQGDRpw/vnnA9CsWTPb/LZZs2Y2c7V58+b2/KZNm9KqVSsA4uPjbUl1Sd9ZRKT0vvnmG1u94PV6ufzyy2O8ovBKypqHMyVMaWlpfm0Upk6dCviXNrmHhrizS+bOnWvfk6dOnWq/g4cT7julGS4SyXe6wMz8aGYOuQV+9w/3HaqksjIofYuN0r7Hh/remJiYCMDAgQO58sorgYqtKImV1NRU24Ta5/NRu3btUFeNaEdWy+CQSStzTwpavHixfZJnZmbaN4hQ6VnVKThk1pCTk0O3bt1KXJP7TWTdunV2DKM7kBFs+1D8oDeSx2/6kEQycrEi9mewN/JI11nWDv2zZ8/2SzM1qZ4TJ04slgYZzeBQ4GMIpTT7OVRZWUWO74xWcMgECi+99FK/94poB4cCmS88Xq/Xb/Sq+SAsb4ne2RIccr9/BN7OPbEy1BQMkYpy5H565MYAACAASURBVMgRALZu3crWrVvtaRP42bZtm00Dz83NZffu3UG3c+GFF9pAiTuA0rRpU3t+06ZNbZClfv36tudAw4YNqVevHuD0DzFllOedd55fCVgwphQs0PHjx0P2IXGXqpkSjKNHj7Jv3z67T0yZW2FhIYcOHbKBrL179/oFwfLz8wEn8GXON/vUMF+A27RpQ3x8PABt27a1p9u3b0+XLl0A6Ny5s/1eV6NGjaDrFxF/CQkJADRq1MiWY4tI1XDixAnAaUHz29/+FoDHH3883E00rUxERERERERERMKrlplDhjsLwuPx2Kj4lClTSvylPTDDxTR6DGxwGmkqnHl8Fb1d97YjVVJGQ2m25/V67ePp3Llz0CZrkWa1RLreSG9XVmW9v/JmigQrh/R4PMyfPx8400gtmplD0c5sCzWt7LrrritX8+DyZG2FU55U12ityUhOTrbPlfL+nc6WzKGSbhfpdUTCMUMBvvzyS5uJnJOTEzRDqKioyGaquLNa2rVrZ0/Hx8fTtm1be9qc37x5c2W5uBw9etRmWOXm5trmse5SPHdG1g8//GCzjuBMllSnTp1sRlGXLl3sQIgrrriCyy67DDjTXFukOtuyZQvgNKf+xz/+AcCtt94ayyWJyP959NFHAXjjjTf45ptvAEoa+hDRQUu1m1bmNmnSJL8x66bPUKgJZW6jR4/2C+L88MMPQPEytKKiIltbaerjDfPFxl3jWprtglO3Gbhds22z3XAHsKU9OIrk+oWFhbaHTEpKil9taklTnNzTAeBMXWppO8gXFhZGbRKRu8u+4Z5YEO52kZRkhZowZeqJu3fvbsv2MjMzg05GqyymVKei9nXjxo3t4/d4PLZPzJQpUxg4cGCZx7SXd99HQ7TXNHny5GLTM8517n0a7jVpyu1EQsnJyeGzzz4DYMOGDfa15PV6/UrATKl0586dueSSSwC4+eab6dy5M+BMouzYsSNAuD4AEoF69erZfdmxY0euvfbaEm9jfmDbunWrnbbjPv3RRx/x6quv2uuaYFzXrl3p2bMn4LQgMG0Ifvazn9nSPpFznQmiTp8+nbvvvhtwAth9+/aN5bJEqr1XX32VF154AYD33nuvQieBqqxMRERERERERKQaq9aZQ1dddZXf/03GTiQZGIMGDbJNgVNSUsLeNtSv18GyH0qz3Ui3XdmlE/Pnz/crfSrNRKDAbIpdu3YB4TOHTHd7d8f7H374IWQz8fIKljm0a9euEjOHxowZE1GmyJgxY4qdFxcXZzPN7rjjDho0aAA4pUmled6WV2pqarH9DKEbt5eXaUrdr18/vF4vS5cuBUo/1axPnz5lvp57Atb777/vN3UhktKlUNctz5pmz57N6tWrAZg2bVrQ5t3uLKzqwv0a++677/xekybLDSLLDpVzm2mq/Mknn7BmzRrAGaCwfv16APLz8+0vcT169LBZJCNGjLBTe3r37k2TJk0qe+lSCubvc/XVV3P11VeHve73339vM8TcJYPz58/nu+++A5z3cTPdpn///jabdcCAAVx22WWcd55+c5Vzz6OPPsqXX34JwPDhw8nIyADgF7/4RSyXJVLtvPTSSwA8+OCDzJ07F4Drr7++Qu+jWgeHGjdu7BdYMOVPkTJlPk2aNLHlUs2aNWPs2LF+o/FMaZRbuIO2cNsFwm67PAeD7oOnYOdHWjoUWDpm1mjGG4aTkJDgV+pnPoB69eplS4w2btzIvffea28zdOjQYtuZN2+eDZZ4vV42bdoEVMzoajO9ITMz0641IyPDBkiys7PZuHEjgN86b7rpJr/Akklrd/N4PNx0001B79dMoZo4caJfSq8p4asMgfvaHZgyX6Q3bdoUlRHhvXr1KnMQqlevXn5B12D7PikpKej2TWDInK6oYGt51rR69Wq/14l5H3M/3zIzM+1z9WwS+J7mfv8J9h7lLh9zv8aeeeYZv35cK1asAJySssCxqnLuOnnyJOC8L7/77rsArFy5ks8//9xe3qlTJ8A5wH/qqacAuOaaa+jevTsANWtW669K1UbHjh1t2dott9zid9mBAwcAWL9+vZ2uuW7dOjsC+8cff6Rx48b2S/rQoUO54YYbAGyJocjZKi4ujr/97W8A3H333fa74F/+8heVaYtEmZlK9uCDD9pjweeff95Oz65o+olDRERERERERKQaq9bTygB8Ph/gNJUMNRksEiZrYv369X4ZJXCm+emVV15pS0QizYAI3C5QbNtXXnkl4JSflDWzorRT1cLxer12spY7q2Ho0KGsXLkS8C8BW758uV/Gk/mbpKWl2fI0j8fDpEmTAEL+6p+Tk8OcOXMAJ6PFbHPSpElRyRTw+Xw2gpuSkhLR/ZmsiGXLlvntA7OPEhISgpanxcXF2Qk57tvOmjXLr8SqtNOz3IqKikLe3v13Nxkuc+bMsZlDkfx9qor09HTb0Bvwa5geTLiyslBKO9GutGvKy8tj2bJlgP9rKTExkYEDBwJOFk1FNQqvzGll4Z6DkTw/Q73GzD6tyP0iVY/P5+Ptt98G4N1337UZq4WFhbYcbMiQITbDo3///jRv3jwma5Wzn3nv+fbbb1m7dq19vr333nvk5+cDcPHFF9ssomHDhtnTmogmZ6v//d//BZyy9nvuuQdwMhn02SpSsb7//nvbDP6zzz6z32VHjBhRls1FdJBY7YNDIiISXmWPsheJ1A8//MAbb7wBwNKlS1m/fj0XXngh4ASBhg0bZk+bEfEi0Xb69Gm++OILwAlSmpLGtWvX2jLF4cOH2/5nI0aMsL0ERc4WmZmZNjhUq1Yt/vKXvwAady9SHidPnmT27NmAMynQlL6//vrrtty9jBQcEpGqryzZTno/qFwKDklV8OOPPwKwYMEC++vZp59+ajN/fvWrXzFq1CibFWTGkotUFfv27bNZnxkZGbz33nuA81wdPnw4AOPGjbOn9RyWqm7v3r2A0290wYIFANx8883MnDkTgMsuuyxmaxM5m5jPg0ceeYStW7cCTu9R08u3du3a5b2LiA641HNIRERERERERKQaU+aQiIiEpcwhiRUzZv6VV15h6dKlANSpU8c+J0eNGsV1110HKMtCzj6m1+WyZctIT08HnDK0Nm3aADB+/HjGjx8PQOvWrWOzSJEIrV69GnCm6n799dcAjB492vYhLWdJjMg5x5QcT58+3U7CHD58uC3RvPjiiyvy7iLKHNJ8Vql2ytu0WUREouPkyZO2ZOz5559n48aNgDNw4a9//SvgNGpXM185FzRp0gRwSsnGjRsHOA1IzbCLl156iRkzZgDOwIrHH38cgCuuuCIGqxUJzwzE2LBhA//6178A+J//+R969uwJOL2IzPCSAQMGxGaRIjF04sQJO1hq1qxZrFu3DnCGpWRnZwPQt2/fmK3v/7d377FV1vcDx9/IzXKxQMtkXAW5KE5A0a3FOUBANKTM4byMzLEQRXRT9Kdx05QlTIxbdDNGow4WF0OmKGw61C0ikGwMynQIBaMUCGgRilAIrVAu5fL749nz9bS0pYW2p+15v/56+vTpcz7n9PScp5/z+Xw/YFuZJEmSJElSSrOtTJJUo/h1OD8/P0za6datG5mZmQBkZGSQkZERtrt161bh63g7cf+5VPCpZTl+/HhoV3ziiSfYvn07AD/+8Y+5//77ASsllJrKy8t58803gaiSbu3atQDk5OTwq1/9CoCRI0cmLT7pTE6dOhWqiH7729/y4YcfAjBs2DBmzJgBRK/16enpSYtRakiFhYWhGvTll1/myy+/BKIplY899hjQaNVCTiuTJJ27+HV4586dfPe73wWiCSXxlJLi4uKwvX//fvbt28fhw4erPFecFKqcQIrHj1eXTMrMzKw24WSL0bmJe9t79OjBlClTAEISsCG98847ADz44IN89tlnANxxxx3k5uYChPGtkiLx38yvf/1r/vvf/wJw00038cwzzwDQr1+/pMUm1cZHH30EwEsvvRRaiE+dOsUPf/hDAG677TbGjx9P27ZtkxajdLZKS0sBWLJkSXh+v/fee1x44YUATJ8+nbvuuguAvn37NnZ4TiuTJEmSJElSzawckiTV6GymlZWVlQFfVxLBmauN4mP2799/2vH79++v9n0gLS0NOL0Cqaa2t8r7K1cypUrb24kTJypUCfXo0QOABx54IHy6FT9e9aGoqAiAWbNmheljt912G08++SQA/fv3r7fbklqyd999F4CHHnqInTt3AlFF0X333Qc0TvWfdC7iKotXX32VV199FYgmVKanp3PTTTcB0bSzcePGAdCuXbvkBCpVo7S0lCVLlgCwaNEi3nvvPSCqkr/hhhsA+MlPfkJOTg6Q9Ndl28okSeeuKYyyP3XqVK0SSHVJRNXU+lY5gdRS29727t3LN77xjdP2t2nTJoyGnzZtWpgwc+mll571bb322mvce++9AHTt2pUXXngBIFxASaq7Y8eOheTqk08+ydChQ4HoH5V6HoMsNbiioiL+8pe/hA8P/v3vf9O5c2cAxo8fz/jx44HofeOiiy5KVphKMadOnWL9+vUAvP/++yEJtGrVKs47L2rEuuGGG0J7ZE5OTnjeNiG2lUmSJEmSJKlmVg5JkmrUFCqHGkJZWVmVFUj79u2juLgY+LrSqKW2vRUUFHDJJZfUeEzbtm05fvw4AGPHjuXhhx8GYOLEieETs+qcPHkyTON46qmnmDVrFgBz585NakXVnj17AFixYkVoZ4hLwxvbmjVreOWVV4BokdaZM2cCMHPmTIYPH56UmNQ8FRQUMG3aNAC2bNnCG2+8ARDacqTmpqioiL///e9AVLGxbNkyIHr/HThwIBC9F40ePRqAUaNG0atXr+QEq2Yvvo7btGkTeXl5rFixAoiee/F1w0UXXcT1118PwPXXXx+2m2ClUGW2lUmSzl1LTQ7Vp8S2t+ra22pqe6sq4VSXtjeoeQJc5URUfNyWLVu47rrran0/27RpExJF/fv356GHHgKi1rNOnTqF4+I1p26//fZwMf/HP/6RqVOn1vq2GtI999wDRMmYWGNfZ8QXnePGjePzzz8HouklCxcuBKJroOoSViUlJXz66acAbNy4kbfffhtIXoIr0cKFC/nRj34Uvo4nttx+++1ndb78/HwAFi9ezNy5c8P+OIl2yy231Ok5vGLFitCy8uKLL1Z7XPyPwN/+9rcwcjsnJyc8h8/2/jS0I0eOADBjxozw2D/33HPh8ZKas5MnTwKwdu1ali5dCsDSpUv5z3/+A8DRo0fDFKhrrrmG7OxsALKyskKy3bWL9NVXXwHR82jVqlUA5OXlkZeXB0TXa+np6YwZMwaIWhonTpwIwKBBgxo/4PphW5kkSZIkSZJqZuWQJKlGVg4lR13a3iofU90EuPp8L23VqlVoK0tLS+Puu+8GoqqcuH3so48+CtUsV111Vb3ddn1JbM9r7OuMxOqlut727Nmzw3ZiNU0yr5XimObOnUtBQUHYP2TIEAByc3N5/PHH63TOFStWVGiJihcEHT58eIXKq+qqkxLbBxOrmWLVPV579uzhzjvvBODtt98OlV35+flMnjz5rO9PY3v66acB+MUvfsGf/vQnIJqcI7U0R48eBaJKkLj6Y9WqVWF79+7dtG3bFoChQ4dy+eWXA9FrSVxRNHz48CoHNKh52r59e6g83bBhQ9jOz89n27ZtQPQeMHjwYACys7MZNWoUELUnDh069Iyt882MbWWSpHNncqj5i99D40RRnDh6/fXXef7554ForH19iVvaVq5cGaYnNUXJTA7V120n8z7E8vPzGTFiRJVxJMaXmNypjcmTJ4eWucrnrer8lb9fXRKtpvMBzJ8/P7SSJR5XWFhIv379wv7ly5cD1KmtLRmeffZZHnnkESCa/nT11VcnOSKpcW3bto21a9cC0evVhg0bwnZhYWE4Lk4OXXrppWFNo8GDB4ftQYMGhbai888/v9HiV+TAgQMAbN26lc2bNwNRe3zidvzhRGlpaZi6Onjw4JAQvOKKKxg2bBgAV199Nd27d2/U+5BEtpVJkiRJkiSpZm2SHYAkSWpYcXVFZmZmWJwaYPXq1eGTtdpWDsXnatOmDeXl5UA0pSOuHFm9ejWvv/46QJOuGjqTuCVpwYIFYUJbTk4ODzzwAFCxWqSkpCRMhkqsOMnNzeW+++4Dok+kq5sy1xjVP4lVNPXdDvXBBx/U6bizncIWt5Jdd911lJSUhP3VLbaceD+rqhyqTmK1UqJ4odtYvLB1U68cmjVrFp988gkA06dPZ926dUD0NyylggEDBjBgwAAgWsQ+UVyNsn79ejZu3AhEk/+2bNkCRK87cXXRiRMnwut17969w2tCr1696N27NwB9+vQJ2z179gzVhpmZmbRv377B7mNzU1ZWRlFREQC7du0K7bs7d+5k586dQFStmbgdvy8D4bEcMGBAaF8ePXp0eA8eMWIEl112GfD11Fidme8KkiSlqH379lWbsIi1a9eOY8eOAdE/k9/5zncAuPHGG5kwYQIAI0eO5NprrwWitXTGjx/fgFE3vMQ1Z6ZOnRoSNolr4Kxfvz4kOX75y1+GyWdffvllmBjVr1+/sD7Uiy++WG27VXNvnf/oo49qdVycdLnrrrtqdfzjjz9eIVETP/bz5s0Lt5mTk8OcOXPqEm6t4zyT+Hde09SzpuLZZ58FovaKOO6f//znyQxJahK6dOkCwJgxY8J0qsriNY22bdsW2pa2bdsWkkY7duxg9erVQJTE+PLLL4HTP3Tp3Llz+ICme/fuFSaJJm537NgRgA4dOtC1a1cgSnDESY4uXbqEY9q1axfWxklPT68y/s6dO1eZDD569GiYLpro5MmTFRLw8TGHDx8O+w8ePMjhw4fD5K+vvvqKgwcPAhXXRiwuLmbv3r2n7a88kTWeIlc50TZ27Fggej+NW/oGDRoUEnPxB1yqH7aVSZIkSZIkpTAXpJYk1cgFqVuuqVOnhklPbdq04eTJk0D0qWE8wWPSpEmhEuh73/senTp1Ou08eXl5oXJo27Ztp7XfNFXVVe8sXLgwTLeqfP0R/0zitKrZs2dXqBA60/nP9L36uA+NqXL1WXUVUlV9/0zihUYffvjhKit68vLyyMrKqnOMNcVxzz33hOoaIFQBVNca2JyuUZ966qnwHN26dWtLm8YjNQnHjx8HoKioiB07dgCwd+9eiouLw3vF3r17w3CI4uLiCtuHDh0CooqduO2tKWrdujUXXHABAJ06dQrXB5mZmWRkZABRJVS80Hfi/szMTHr06AFE1ULxthpMrRaktq1MkqQUFa/BANEY8LhNbMKECXzzm9+s9XnefPNNrrzySuD0dVmao1dffTVsV9d2N3fu3JAcSlzbprCwMKxFo3N34YUXAtFaEnEbX+L6QdnZ2WGtivp67k2bNq1Ccujjjz8GTl9bKB4V35zcfPPNYXLZunXrGDlyZJIjklqeuIWrT58+9OnTp17OWVZWFlqxSkpKQgtXeXl5WP8v3lfZgQMHqkxit23btsoPfODrdrtWrVqFdra0tLSwv2PHjqEVTC2HHxdIkiRJkiSlMCuHJElKUXPnzq3TFKfq5OfnM2zYsHqIqGlIbF+qbcvQ/Pnzw8/GFSXxlLOWLicnp1aLOOfk5NTpvJs3bw6P4WOPPRbaxy677LLQ9gdfP/b1NYUtKyuLvLw8AF555ZWwEHblSqFrrrmmXm6vMQ0YMCAsZJufn2/lkNRMdOjQgQ4dOgCE1iypvpkckiRJ56SsrCxctLY0mzdvDusvVWfhwoVhfO7nn3/eIlrrzjTFLjFp1lDJoWeeeSacd8mSJWH/7bffXiE5FCc46ys5BIREVFZWVlijZ8WKFeH7M2fOrNVaR01RvEZIvK6JJElgW5kkSZIkSVJKs3JIkiSdkwsvvJA9e/YkO4x6M2/evFAJtGDBgtDalJ6eHu7nggULeOihhwAqVLG0hKohqNsErm9/+9tndVxciTNu3DiWL18OVFz0OXFR6JrMnDmzVsedrfz8fCCKM65+mjNnToPeZkMpLy8P05KcDiRJStRskkO33nprskOQpJQUT16aOnVqkiNRU5Wdnc2zzz4LREmFM7UkJVtViaw9e/aEcbvf//73Q3KounWZ4glZULGtqrCwkCNHjtR4/jjZEIvHtZ+pfS1RSUlJlfvS09OrPH727Nlhuz7brwCGDx9Obm4uED1e8f1JlJubG6aNxeK1fBK3E5NSS5YsYfLkyUCUSIoTR2vWrKlwnjMlhwoLC6vcX1PLYPw7WrZsWUgO5ubmct999wGE32Vz8+GHH4Yx29nZ2UmORpLUlNhWJkmSJEmSlMJa1aVsuAFVGcTu3bt58MEHAThx4kSjBiRJquiOO+6o84KySg1FRUX069cPgKVLlzJmzJjkBnQG1VU2JV4TxdUm8+fPD5VDM2fO5NFHHwUqto/l5+czYsQIoGJ1yXPPPRdaeB599NHwGNWkNtdltanMqnyehqwcSrRw4cIKbXavvfYaEC0iXdmZ2soSj1m0aFGFNrN4clhOTk611T91qWBLfLxatWoVqpFGjx7NlVdeCdStsqupmjFjBp999hkQ/a1KklJCrd4Qm3RySJIkNQ+zZs0CYO3atfzzn/8EoHXr1skMSdL/bNq0CYArrrgiJOJGjRqVzJAkSY2nVskh28okSZIkSZJSmJVDkiTpnB04cACASy65hLvuugto2PYlSbVTVlZGVlYWEC0evmDBgiRHJElqZLWqHGo208okSVLT1aVLFwBeeeUVJk2aBESjsn/2s58lMywpZR09ehSI1mWKJ+g9//zzyQxJktSE2VYmSZIkSZKUwqwckiRJ9WbixIm88MILANxzzz3s2rULiFrMzjvPz6SkxrB7926mTJkCwPbt21m5ciUA6enpyQxLktSEmRySJEn1asaMGQB07NiRO++8E4BPPvkkrHXSqVOnpMUmtWTr1q0DYPLkyXTu3BmAlStXMnDgwGSGJUlqBvwIT5IkSZIkKYU5rUySJDWYNWvWADBlyhQuuOACAF588UXGjh2bzLCkFuP48eMA/O53v2POnDkAjB49moULFwK2kkmSajetzOSQJElqcLt27WLmzJkAvPPOO0ybNg2Ap59+moyMjGSGJjVbq1evDn9XW7duZfbs2QA88sgjtG7dOpmhSZKajlolh2wrkyRJkiRJSmFWDkmSpEb117/+lfvvvx+Ao0eP8sgjjwDRdDMXq5ZqVlBQwBNPPAHAn//8Z8aNGwdE7ZoXX3xxMkOTJDVNtpVJkqSmqbS0FIDf/OY3PPfccwCkpaXx8MMPA3DvvfeaKJL+p6CggLlz5wLw2muvMWjQIADmzJnDrbfemszQJElNn21lkiRJkiRJqpmVQ5IkKan27dsHwO9///tQRdSuXTumT58OwIwZMxg4cGDS4pMa08mTJwFYtmwZf/jDHwB46623uOSSSwDIzc3ltttuA+C88/ycV5J0RraVSZKk5iVOFL300kvMnz8fgMLCQsaPHw/A3XffzeTJkwFo27ZtcoKU6tmePXsAePnll8Pzfvv27YwZMwaI1uO6+eabARNCkqQ6s61MkiRJkiRJNbNySJIkNUlxe80//vEP5s2bB8C7775L165dAZgyZQq33HILAGPHjqV169bJCVSqo/379/PWW28BsGjRIpYvXw5A586d+elPfwpE7ZRDhgxJVoiSpJbDtjJJktSy7Ny5kzfeeAOI/qles2YNABkZGfzgBz8IrTejR4/m/PPPT1qcUmz37t1AlNhcvHgxAMuXL6ddu3YATJo0KSQ5c3JyaN++fXIClSS1VLaVSZIkSZIkqWZWDkmSpGZrx44dACxevJjFixeHSqL27dtz7bXXAjBx4kQmTJgAwOWXX56cQNXiHTlyBICVK1fy/vvvA7B06VI2bNgAQMeOHZk0aRIAt9xyCzfeeCMAHTp0SEK0kqQUYluZJElKLfG0s2XLlrF06VIg+gf9iy++AKBnz56MHj0agOzsbLKysgAYMWKE089UayUlJeTl5QGQl5fHqlWrwtdHjhxh5MiRAIwfP54bbrgBgKysrNBKJklSI7KtTJIkSZIkSTWzckiSJLV4n376KQDvvfce//rXv4Co4iNeLDgtLY2rrroKgFGjRoXtESNGMGDAAADOO8/P1FJFWVkZAJ988gnr1q0DYM2aNXzwwQdhf2zo0KGMGjWK6667DoBx48aRmZnZyBFLklQt28okSZJqsm3bNgBWr14d2oJWrVoV/vkvLy+nU6dOAHzrW99i+PDhAAwfPjysXzRkyBC6d+/e2KHrLJ04cQKAzz77jE2bNgGwYcMG1q9fH7a3bNkSjk1PTwfg6quvZtSoUUDUkpidnQ0Qvi9JUhNlW5kkSZIkSZJqZuWQJElSJceOHQPg448/ZuPGjQDk5+eHyVPr1q1j//794fguXboAMGjQIAYNGhS2Bw8eDMDFF19Mnz59AOjRo4ctag3gyJEjYeHxwsLCUP2zZcuWsF1QUMD27duB6Hcc/x769+/PFVdcAUQT7eIKsWHDhtG/f/9GvR+SJNUz28okSZIaSlFREQCbNm1i69atAGzevDlsFxQUhLa1o0ePhp9r27YtPXr0AKBv37707t0bgF69etG3b18AMjMzycjICO1q3bt3JyMjA4hGordU+/bto7i4uMrt+PH+4osvQhJox44d7Nq1C4A9e/ZUOFe3bt0AGDhwYEjSDR48uELybsiQIQChdVCSpBbItjJJkiRJkiTVzMohSZKkBhIvfvzFF1+wY8eOsB1XviTu37lzZ9jeu3cv5eXlVZ4zLS0tVBFlZmbSoUMHADp06EDXrl3DMfH+9PT0UG3Url27cJ742Mo6duxY4bjYoUOHQrtdomPHjnHo0KHw9cGDBwE4fPgwX331FQClpaUcPnw4nKekpCT8THFxMfv27avweFUVU8+ePYGowipu0evduze9iNur3wAAAcxJREFUevUCoE+fPqEKq2/fvk4MkyQpYluZJElSc1VaWsrevXuBKIFSXbtVnGQpKyvjwIEDYTsex15aWhoSNuXl5SEBU1paWu3tVpWkSUtL4/zzzz9tf+vWrbngggvC13EiKi0tLezv3LkzaWlpQNTClZiwysjICImc7t27h+24tQ6o8nYlSVKt2FYmSZIkSZKkmlk5JEmSJEmS1DJZOSRJkiRJkqSamRySJEmSJElKYSaHJEmSJEmSUpjJIUmSJEmSpBRmckiSJEmSJCmFtUl2AP/zf8kOQJIkSZIkKRU1lVH2kiRJkiRJSgLbyiRJkiRJklKYySFJkiRJkqQUZnJIkiRJkiQphZkckiRJkiRJSmEmhyRJkiRJklKYySFJkiRJkqQUZnJIkiRJkiQphZkckiRJkiRJSmEmhyRJkiRJklKYySFJkiRJkqQUZnJIkiRJkiQphZkckiRJkiRJSmEmhyRJkiRJklKYySFJkiRJkqQUZnJIkiRJkiQphZkckiRJkiRJSmEmhyRJkiRJklKYySFJkiRJkqQUZnJIkiRJkiQphZkckiRJkiRJSmEmhyRJkiRJklKYySFJkiRJkqQUZnJIkiRJkiQphf0/iEqkOWVf2FEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [20, 20]\n",
    "\n",
    "for i in range(0, gbm.num_trees()):\n",
    "    ax = lgb.plot_tree(gbm, tree_index = i)\n",
    "    plt.show()\n",
    "    \n",
    "    if i == 2:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_proba = predictions[::,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)\n",
    "auc = metrics.roc_auc_score(y_test, y_pred_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17803d51208>]"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QG/T9/af0EeLtSYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running ensemble model directly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ref: https://github.com/serengil/deepface/blob/master/models/face-recognition-ensemble-model.txt\n",
    "deepface_ensemble = lgb.Booster(model_file= 'face-recognition-ensemble-model.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "#bulk predictions\n",
    "#bulk_predictions = deepface_ensemble.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "verified:  True\n"
     ]
    }
   ],
   "source": [
    "#single prediction\n",
    "idx = 0\n",
    "verified = np.argmax(deepface_ensemble.predict(np.expand_dims(df.iloc[idx].values[0:-1], axis=0).shape)[0]) == 1\n",
    "print(\"verified: \", verified)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
